AI, LLMs and modern ways of working
AI consulting for companies in Switzerland
digitario helps organisations use AI and LLMs where product work, delivery, and engineering processes need to become clearer, faster, and more focused.
For many companies, AI is strategically important but still operationally vague. Between tool excitement, security questions, and unclear expectations, what is often missing is a realistic frame for where modern AI support creates actual value.
digitario helps teams make that distinction: pragmatic, business-oriented, and focused on product, delivery, and digital execution. Not with generic AI promises, but with a clear view of where AI genuinely helps day-to-day work and where it does not.
The difference to abstract consulting: Claude Code, OpenAI, and local LLMs are in daily active use at digitario, not as experiments, but as real working tools. What gets recommended here comes from hands-on practice.
02 · What this is really about
Use AI where product and delivery work become more effective.
The goal isn't AI for its own sake, it's making real working methods measurably better.
Requirements work becomes clearer and better structured, concept and alignment cycles get shorter, prototyping speeds up, and engineering work gets meaningful support. Teams gain clarity on how to use new capabilities without turning AI into a source of confusion, tool sprawl, or vague expectations.
In enterprise contexts, the value doesn't come from isolated prompts, it comes from embedding AI into real collaboration. That means clear task definitions, transparent review steps, and a solid connection to product, delivery, and engineering work. What matters is evaluating AI against real tasks rather than chasing tools, and building in context, approvals, and review from day one.
A typical engagement flow
Understand the situation, goals and areas to act on
MENSCHWhere is the real value? Where isn't it?
KISet roles, review and approvals
MENSCHTest the first workflows in real teamwork
KIEstablish a sustainable way of working
MENSCHTypical starting points
This work is most valuable where AI is creating real urgency but hasn't yet been turned into a reliable way of working.
For example: a company wants to use AI but doesn't yet have a clear picture of where the real impact lies. Or teams are evaluating modern tools but need grounded guidance on value, effort, responsibility, and security. Or early AI initiatives exist, but they need more structure and a practical model for day-to-day use.
What digitario actually takes on
digitario helps identify useful AI and LLM use cases in the specific business context and translate them into clear product, delivery, and engineering workflows.
This also includes facilitation between business, product, IT, and management, plus a pragmatic frame for roles, expectations, and governance. Once AI touches multiple teams or roles, how you work with it matters more than which tool you pick.
- AI-supported research and structuring
- Requirements management and specification
- Prototyping and concept work
- Support in engineering processes
- Documentation, summaries, and decision support
- Make review and approval steps explicit
What improves
Not every organisation needs maximum AI adoption. What matters is where AI reduces effort and sharpens decisions. Teams develop a clearer sense of which use cases work, and where holding back is the smarter move.
Product, delivery, and engineering pick up speed without sacrificing quality or accountability. And AI isn't treated as a separate initiative, it becomes part of sensible workflows and clear responsibilities.
03 · Where AI is applied
Where AI gets to work at digitario.
Product & requirements work
Research, requirements and specs become faster and better structured, with clear review logic and no loss of control.
Delivery & engineering processes
Coding assistants, agentic workflows and prototyping support exactly where teams feel real relief.
Governance & framing
Clear rules for data, roles, responsibilities and approvals, so AI use never becomes an uncontrolled side channel.
04 · The difference
Not from theory. From daily practice.
digitario uses Claude Code, OpenAI/Codex, local LLM setups and OpenClaw instances every day in its own work. This website was built with them, so the guidance comes not from slides but from lived practice.
That matters, because AI consulting built only on external benchmarks and vendor promises doesn't know the practical pitfalls of everyday work.
In daily use
- Claude Code for development and architecture
- OpenAI/Codex for workflows and research
- Local LLM setups for sensitive contexts
- OpenClaw instances for autonomous agents
- Agentic coding workflows in real projects
05 · In practice
From framing to a working setup.
A digitario engagement doesn't start with tool selection but with the question: what exactly should improve?
From there come clear use cases, a pragmatic adoption mode and a working model that genuinely holds up in the team.
- Identify and prioritise sensible use cases
- Define work logic, review and accountability
- Run a pilot in a concrete team context
- Set up a pragmatic governance framework
06 · FAQ
Common questions about AI consulting at digitario
It's not abstract innovation consulting. It's the practical assessment and design of AI-enabled ways of working across product, delivery, and engineering.
Essentially for everyone, because the opportunities exist in almost every company today. The real skill is recognising where AI delivers genuine value, and where it merely burns tokens so someone can claim to use AI. That is why a thorough analysis always comes first. The outcome may even be that AI adds little for the originally intended purpose, but considerably more somewhere else. It is individual and best assessed in an intro call.
No. ChatGPT, OpenAI/Codex, Claude Code, or self-hosted LLMs are means to an end. What matters is which working model makes sense in your specific context.
Yes, as long as governance, data handling, tool boundaries, and responsibilities are addressed properly.
No. Most engagements start with an assessment and a focused pilot. Technical prerequisites are clarified along the way, not as a precondition.
digitario comes from delivery, not from presentations. Three perspectives come together here: product management, engineering, and project leadership. That makes it possible to apply AI exactly where it is genuinely needed, from hands-on daily practice rather than vendor briefings. One example: with specialised frameworks, agentic teams take on a clear role and deliver not just critical review, but genuinely high-quality code.
07 · Contact
Assess AI realistically instead of discussing it vaguely.
If you're ready to move beyond watching AI from the sidelines and want to integrate it meaningfully into product and working processes, a short conversation is often enough to map out starting points, opportunities, and constraints.